In this paper, we propose a novel CNN architecture that is well-balanced between efficiency and accuracy. Inspired by point-voxel CNN[1]}, we construct our network using point-voxel layer that takes advantages of both sparse point representation and volumetric convolution. Our point-voxel layer consists of two parallel branches, a voxel-based branch which aggregates local neighboring features, and a point-based branch which maintains fine-grained point-wise features. During discretization in voxel branch, we aggregate neighboring features on non-empty voxel centers and use standard 3D convolutions to enhance local feature encoding. Voxel features are propagated back to point domain through devoxelization. Outputs from point and voxel branches are fused self-adaptively via a feature selection module(FSM), which learns channel-wise attention for both branches.
